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“Prior Probabilities” is a category for books that explore Bayesian thinking—how we start with prior beliefs or knowledge and update them as new evidence arrives. It includes accessible primers on probabilistic reasoning and statistics, practical guides to Bayesian inference and decision-making, and cross-disciplinary works showing applications in data science and machine learning, medicine and diagnostics, economics and finance, law and public policy, forecasting and risk analysis. The category also encompasses cognitive psychology and philosophy of science on how priors shape judgment, as well as histories, biographies, and case studies tracing the development from Bayes and Laplace to modern practice. Expect both conceptual overviews and hands-on approaches to modeling, choosing priors, calibration, and real-world decisions under uncertainty.

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